In this paper, we propose a novel ellipse detection method which is based on a modified
RANSAC, with automatic sampling guidance from the edge orientation difference curve. Hough
Transform family is one of the most popular and methods for shape detection, but the Standard
Hough Transform loses its computation efficiency if the dimension of the parameter space gets
high. Randomized Hough Transform, an improved version of Standard Hough Transform has
difficulty in detecting shapes from complicated, cluttered scenes because of its random sampling
process. As a pre-process for random selection of five pixels to be used to build the ellipse's
equation, we propose a two-step algorithm: (1) region segmentation and contour detection by
mean shift algorithm (2) contour splitting based on the edge orientation difference curve obtained
from the contour of each region. In each contour segment obtained by step (2), 5 pixels are
randomly selected and the modified RANSAC is applied to the 5 pixels so that an accurate ellipse
model is obtained. Experimental result show that the proposed method can achieve high
accuracies and low computation cost in detecting multiple ellipses from an image.

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Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews